| Signal | Pixtral 12B | Delta | Qwen3.5-Flash |
|---|---|---|---|
Capabilities | 33 | -50 | |
Pricing | 0 | 0 | |
Context window size | 72 | -23 | |
Recency | 31 | -69 | |
Output Capacity | 20 | -60 | |
Benchmarks | 0 | -67 | |
| Overall Result | 0 wins | of 6 | 6 wins |
0
days ranked higher
0
days
30
days ranked higher
Mistral AI
Alibaba
Pixtral 12B saves you $4.50/month
That's $54.00/year compared to Qwen3.5-Flash at your current usage level of 100K calls/month.
| Metric | Pixtral 12B | Qwen3.5-Flash | Winner |
|---|---|---|---|
| Overall Score | 38 | 79 | Qwen3.5-Flash |
| Rank | #292 | #80 | Qwen3.5-Flash |
| Quality Rank | #292 | #80 | Qwen3.5-Flash |
| Adoption Rank | #292 | #80 | Qwen3.5-Flash |
| Parameters | 12B | -- | -- |
| Context Window | 33K | 1000K | Qwen3.5-Flash |
| Pricing | $0.10/$0.10/M | $0.07/$0.26/M | -- |
| Signal Scores | |||
| Capabilities | 33 | 83 | Qwen3.5-Flash |
| Pricing | 0 | 0 | Qwen3.5-Flash |
| Context window size | 72 | 95 | Qwen3.5-Flash |
| Recency | 31 | 100 | Qwen3.5-Flash |
| Output Capacity | 20 | 80 | Qwen3.5-Flash |
| Benchmarks | -- | 67 | Qwen3.5-Flash |
Our composite score (0–100) combines six weighted signals: benchmark performance (25%), pricing efficiency (25%), context window size (15%), model recency (15%), output capacity (10%), and capability versatility (10%). Here's what the scores mean for these two models:
Scores 38/100 (rank #292), placing it in the top 0% of all 290 models tracked.
Scores 79/100 (rank #80), placing it in the top 73% of all 290 models tracked.
Qwen3.5-Flash has a 41-point advantage, which typically translates to noticeably stronger performance on complex reasoning, code generation, and multi-step tasks.
Qwen3.5-Flash offers 38% better value per quality point. At 1M tokens/day, you'd spend $3.00/month with Pixtral 12B vs $4.88/month with Qwen3.5-Flash - a $1.88 monthly difference.
Both models have comparable response speeds. For most applications, the latency difference is negligible.
When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.
Code generation & review
Higher benchmark score (0/100) indicates stronger performance on coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Faster response time (speed score 0/100) is critical for user-facing chat. Pixtral 12B also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (1000K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.10/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (79/100) correlates with better nuance, coherence, and style in long-form content
Image understanding & OCR
Supports vision input - can analyze screenshots, diagrams, photos, and scanned documents directly
Qwen3.5-Flash clearly outperforms Pixtral 12B with a significant 41.00000000000001-point lead. For most general use cases, Qwen3.5-Flash is the stronger choice. However, Pixtral 12B may still excel in niche scenarios.
Best for Quality
Pixtral 12B
Marginally better benchmark scores; both are excellent
Best for Cost
Pixtral 12B
38% lower pricing; better value at scale
Best for Reliability
Pixtral 12B
Higher uptime and faster response speeds
Best for Prototyping
Pixtral 12B
Stronger community support and better developer experience
Best for Production
Pixtral 12B
Wider enterprise adoption and proven at scale
by Mistral AI
| Capability | Pixtral 12B | Qwen3.5-Flash |
|---|---|---|
| Vision (Image Input) | ||
| Function Callingdiffers | ||
| Streaming | ||
| JSON Modediffers | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
Mistral AI
Alibaba
Pixtral 12B saves you $0.1290/month
That's 30% cheaper than Qwen3.5-Flash at 1,000 tokens/request and 100 requests/day.
Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.
| Parameter | Pixtral 12B | Qwen3.5-Flash |
|---|---|---|
| Context Window | 33K | 1M |
| Max Output Tokens | -- | 65,536 |
| Open Source | Yes | No |
| Created | Sep 10, 2024 | Feb 25, 2026 |
Qwen3.5-Flash scores 79/100 (rank #80) compared to Pixtral 12B's 38/100 (rank #292), giving it a 41-point advantage. Qwen3.5-Flash is the stronger overall choice, though Pixtral 12B may excel in specific areas like cost efficiency.
Pixtral 12B is ranked #292 and Qwen3.5-Flash is ranked #80 out of 290+ AI models. Rankings use a composite score combining benchmark performance (25%), pricing (25%), context window (15%), recency (15%), output capacity (10%), and versatility (10%). Scores update hourly.
Pixtral 12B is cheaper at $0.10/M output tokens vs Qwen3.5-Flash's $0.26/M output tokens - 2.6x more expensive. Input token pricing: Pixtral 12B at $0.10/M vs Qwen3.5-Flash at $0.07/M.
Qwen3.5-Flash has a larger context window of 1,000,000 tokens compared to Pixtral 12B's 32,768 tokens. A larger context window means the model can process longer documents and conversations.